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Sparse Learning

Course content

​A sparse statistical model is one having only a small number of
nonzero parameters. Examples of models and problems that will be
considered in the course are: regression models, matrix
decompositions and Gaussian graphical models.

The theme of the course is estimation and statistical inference
with sparsity inducing penalties. Lasso and its variations are
the a main examples. Sparse estimation is often achived via
convex optimization, and this theory will also be treated in
the course.

In the course there will be a focus on models
and algorithms, and how to apply them to real problems. Some
results from the statistical theory will be touched upon, but it
will not be a main part of the course.